Home / Can I run it? / Llama 3.2 3B / Llama 3.2 3B on an Arc B580

Yes. Llama 3.2 3B at FP16 needs about 8.4 GB, and the Intel Arc B580 has 12.0 GB usable at a ~4k context.

Can I run Llama 3.2 3B on an Arc B580?

Llama 3.2 3B at FP16 needs about 8.4 GB, and the Intel Arc B580 has 12.0 GB usable. That figure is 12 GB of VRAM on the spec sheet, of which 12.0 GB is usable for weights and cache, at 456 GB/s memory bandwidth. A tiny Meta Llama, aimed at phones and low-VRAM cards. Quality is limited; it is the model you pick when memory is tight. These are estimates from our formula, not measured benchmarks.

Quant that fits

FP16 Fits with headroom

Memory needed

8.4 GB at FP16, ~4k context

Usable VRAM

12.0 GB of 12 GB VRAM

Est. tokens/sec

43–60 estimate

Llama-3.2-3B-Instruct · Intel Arc B580 · Everything this machine runs · Q4 vs Q8 vs FP16

Memory at each quant

Estimated tokens per second

Estimate: 43–60 tokens/sec at FP16. This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from How we calculate.

Context headroom

At FP16, this machine has headroom out to about 32k tokens under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

From the model’s config. Try other sizes in the VRAM calculator or see the context length guide.

What to do instead

• See the best GPU for this model family and the Llama-3.2-3B-Instruct model page.

Other hardware for Llama 3.2 3B

• Llama 3.2 3B on an RTX 3060 12GBFits
• Llama 3.2 3B on an RTX 3070 TiFits
• Llama 3.2 3B on an RTX 4060 Ti 16GBFits
• Llama 3.2 3B on an RTX 4090Fits
• Llama 3.2 3B on a Mac mini 16GBFits

Other models on an Arc B580

• Qwen3-32B on an Arc B580Won’t fit
• Qwen3-30B-A3B on an Arc B580Won’t fit
• Qwen3.8-27B on an Arc B580Won’t fit
• Gemma 3 27B on an Arc B580Won’t fit
• Gemma 4 31B on an Arc B580Won’t fit
• gpt-oss-20b on an Arc B580Won’t fit